Another strength of DeepJSCC is that it learns a communication scheme from scratch, optimizing all transformations in a data-driven manner using autoencoders [1]} with a non-trainable differentiable channel model in the bottleneck layer.
This simplifies the JSCC design procedure, and allows adaptation to any particular source or channel domain and quality measure.
Part of that simplification stems from the fact that DeepJSCC not only combines source and channel coding into one single mapping, but it also removes the constellation diagrams used in digital schemes.
In digital communications, channel encoded bits are mapped to the elements of a two-dimensional finite constellation diagram, such as quadrature amplitude modulation (QAM), phase shift keying (PSK), or amplitude shift keying (ASK).
In contrast, in DeepJSCC, the encoder can transmit arbitrary complex-valued channel symbols, within a power constraint.
However, this can hinder the adoption of DeepJSCC in current commercial hardware and standardized protocols, which are constrained to produce fixed sets of symbols.
